This application relates generally to biometrics. More specifically, this application relates to systems and methods for improved biometric feature definition.
Achieving adequate performance from a biometric system relies fundamentally on the ability to isolate biometric features within a dataset from other portions of the dataset—the biometric features may then be used to identify a person, while the other portions of the dataset are generally unrelated to a person's identity. Consider, for example, the case of a fingerprint sensor. Each time a fingerprint image is collected, it is affected by a variety of phenomena that make the image unique. This is true even when the same fingerprint is being imaged. For example, each finger placement on the sensor results in a different portion of the finger coming into contact with the sensor and being imaged. Differences in orientation and pressure of the finger cause distortions of the skin, changes in image contrast, and other artifacts that affect the characteristics of the fingerprint image. The sensor itself may also introduce artifacts into the image that vary from image to image. These sensor artifacts may include fixed-pattern noise that varies in some manner across the image plane and a variety of high-frequency image noise sources, including shot noise and dark noise among other types of noise sources.
Because of the presence of these kinds of nonbiometric sources of variation, two fingerprint images cannot generally be compared directly by a simple operation such as an image subtraction to determine whether they originate from the same person. Instead, salient features of the image are identified, both in the images used to populate an enrollment database and in test images. These features are then compared to determine whether a sufficient number are present and in approximately the same relative spatial location in the two images. If so, the images are said to match; if not, the images are determined not to match.
Many existing fingerprint sensors require direct contact between the finger and the sensor to collect an image. In cases where the finger is not making adequate contact with the sensor, the area of direct contact between the finger and the sensor is reduced, resulting in the collection of less biometric information. Generally, fewer biometric features can be extracted from this reduced image area, resulting in a degraded ability to properly determine matching fingerprint images.
To address some of these deficiencies with properly defining fingerprint features, many systems require that the user take more than one sample for enrollment in the system database. In this way, multiple images may be acquired of the same finger and analyzed to detect features that are common across each enrollment image. But the ability to determine the presence of a true biometric feature is still compromised by the differences in finger orientation, translation, rotation, distortion, and other image artifacts across the set of enrollment images. In addition, the ability to collect and compare multiple fingerprint images is usually only viable during the enrollment procedure. During the normal execution of biometric functions such as identification or verification, most applications require that the biometric sensor operate using a single, rapidly acquired fingerprint image. In such scenarios, there is no opportunity to enhance the feature detection of a test sample by using multiple images.
Fingerprint sensors also typically collect images that originate with the external characteristics of the skin of a finger. But these external characteristics are subject to wear, contamination, or changes that result from differences in environmental conditions, all of which may further compromise the definition of fingerprint features. Furthermore, the surface characteristics are relatively easy to replicate based upon a latent print of the fingerprint left on a smooth, nonporous surface. Thus, the reliance of conventional fingerprint sensors on measuring only the surface characteristics of the finger has a number of negative consequences. First, the number and quality of biometric features that may be detected is limited to those features present on the surface skin, which may be worn or missing. Second, a sensor that relies exclusively on features present on the external surface skin is susceptible to a security breach using an artificial replica of an authorized fingerprint pattern.
Because the finger is an approximately cylindrical object, there is a tendency for the skin to pull away from the sensor surface towards the edges of the imaging region. For this reason, fingerprint images collected for law-enforcement applications are typically collected using a “rolled” procedure. In such a procedure, the image of the finger is acquired as the finger is rolled along the sensor surface so that more portions of the finger come into contact with the sensor to permit them to be imaged. This procedure is time consuming, awkward for the user, and generally requires a skilled operator to assist the proper collection of such data. Consequently, this method of fingerprint image collection is not generally used by automated and unattended biometric sensors, even though the greater image area could in principle provide improved performance.
There is accordingly a general need in the art for improved methods and systems for collecting biometric measurements form which biometric features may be defined.